Category Archives: quantitative research

Credible sources often disagree on technicalities. Sometimes this includes classification of research design. Some argue that there are only 2 categories of research design:

True experiments. True experiments have 3 elements: 1) randomization to groups, 2) a control group and an 3) intervention; and

Non-experiments. Non-experiments may have 1 to none of those 3 elements.

Within-subject Control Group

Fundamentally, I agree with the above. But what about designs that include an intervention and a control group, but Not randomization?

Those may be called quasi-experiments; the most often performed quasi-experiment is pre/post testing of a single group. The control group are subjects at baseline and the experimental group are the same subjects after they receive a treatment or intervention. That means the control group is a within-subjects control group (as opposed to between-group control). Quasi-experiments can be used to answer cause-and-effect hypothesis when an experiment may not be feasible or ethical.

One might even argue that a strength of pre/post, quasi-experiments is that we do Not have to Assume that control and experimental groups are equivalent–an assumption we would make about the subjects randomized (randomly assigned) to a control or experimental group. Instead the control and experimental are exactly equivalent because they are the same persons (barring maturation of subjects and similar threats to validity that are also true of experiments).

I think using the term quasi-experiments makes it clear that persons in the study receive an intervention. Adding “pre/post” means that the

Baseline ->Intervention->Post

researcher is using a single group as their own controls. I prefer to use the term non-experimental to mean a) descriptive studies (ones that just describe the situation) and b) correlation studies (ones without an intervention that look for whether one factor is related to another).

Like this:

Actually when it comes to quantitative data, there are 4 levels, but who’s counting? (Besides Goldilocks.)

Nominal (categorical) data are names or categories: (gender, religious affiliation, days of the week, yes or no, and so on)

Ordinal data are like the pain scale. Each number is higher (or lower) than the next but the distances between numbers are not equal. In others words 4 is not necessarily twice as much as 2; and 5 is not half of 10.

Interval data are like degrees on a thermometer. Equal distance between them, but no actual “0”. 0 degrees is just really, really cold.

In honor of Nurse Week, I offer this tribute to the avante garde research work of Florence Nightingale in the Crimea that saved lives and set a precedent worth following.

Nightingale was a “passionate statistician” knowing that outcome data are convincing when one wants to change the world. She did not merely collect the data, but also documented it in a way that revealed its critical meaning for care.

As noted by John H. Lienhard (1998-2002): “Once you see Nightingale’s graph, the terrible picture is clear. The Russians were a minor enemy. The real enemies were cholera, typhus, and dysentery. Once the military looked at that eloquent graph, the modern army hospital system was inevitable. You and I are shown graphs every day. Some are honest; many are misleading….So you and I could use a Florence Nightingale today, as we drown in more undifferentiated data than anyone could’ve imagined during the Crimean War.” (Source: Leinhard, 1998-2002)

As McDonald (2001) writes in the BMJ free, full-text, Nightingale was “a systemic thinker and a “passionate statistician.” She insisted on improving care by making policy & care decisions based on “the best available government statistics and expertise, and the collection of new material where the existing stock was inadequate.”(p.68)

Moreover, her display of the data brought its message home through visual clarity!

Thus while Nightingale adhered to some well-accepted, but mistaken, scientific theories of the time (e.g., miasma) her work was superb and scientific in the best sense of the word. We could all learn from Florence.

CRITICAL THINKING: What issue in your own practice could be solved by more data? How could you collect that data? If you have data already, how can you display it so that it it meaningful to others and “brings the point home”?

one group is a control group that gets a placebo or some inert treatment so that outcomes in that group can be compared to the group(s) that did get the treatment.

Non-experimental design in which the researcher doesn’t manipulate anything, but just observes & records what is going on. Some of these are descriptive, correlational, case, or cohort study designs for example.

One particularly interesting “experimental” design is one in which 1 or 2 of the experimental design ideal requirements as listed above are missing. These are called quasi-experimental designs.

In a quasi experimental design

The researcher manipulates some variable, but….

Either the participants are NOT randomly assigned to groups

&/OR there is no control group.

A quasi-experimental design is not as strong as a true experiment in showing that the manipulated variable X causes changes in the outcome variable Y. For example, a true experimental study with manipulation, randomization, and a control group would create much stronger evidence that hospital therapy dogs really reduced patient pain and anxiety. We would not be as confident in the results of a quasi-experimental design examining the exact same thing. In the next blog, we’ll examine why.

Critical thinking: Go to PubMed & use search terms “experiment AND nurse” (without the quotation marks). Open an interesting abstract and look for the 3 elements of a classic experimental design. Now look for “quasi experiment AND nurse” (without the quotation marks.) See what element is missing!

Key point! The data collection section of a research article includes: who collects what data when, where & how.

In previous blogs we’ve looked at title, introduction, and other elements of methods section (design, sample, & setting). In this one let’s take a look at data collection.

Data are a collection of measurements. For example, student scores on a classroom test might be 97, 90, 88, 85, & so on. Each single score is a datum; collectively they are data.

What data are collected is answered in this section. The data (or measurements) can be numbers OR words. For example, numbers data might include patient ratings of their pain on a 0-10 scale. An example of word data would asking participants to describe something in words without counting the words or anything else. For example, word data might include patient descriptions pain in words, like “stabbing,” “achy,” and so on. Sometimes a researcher collects both number and word data in the same study to give a more complete description. You can see how knowing the patient’s pain rating and hearing a description would give you a much clearer picture of pain.

Studies reporting data in numbers are called quantitative studies

Studies reporting data in words/descriptions are called qualitative studies

Studies reporting number & word data are called mixed methods studies

How the data are collected includes what instrument or tool was used to gather data (e.g., observation, biophysical measure, or self-report) and how consistently & accurately that tool measures what it is supposed to measure (e.g., reliability & validity). Also included is who collected the data and the procedures that they followed—how did they obtain consent, interaction with subjects, timing of data collection and so on.

Like this:

Question: What is a randomized controlled trial (RCT)?And why should I care?

Answer: An RCT is one of the strongest types of studies in showing that a drug or a treatment actually improves a symptom or disease. If I have strep throat, I want to know what antibiotic works best in killing the bacteria, & RCTs are one of the best ways to find that answer.

In the simplest kind of RCT, subjects are randomly assigned to 2 groups. One group gets the treatment in which we are interested, & it is called the experimental group. The other group gets either no treatment or standard treatment, & it is called the control group.

Here’s an example from a study to determine whether chewing gum prevents postoperative ileus after laparotomy for benign gynecologic surgery: A total of 109 patients were randomly assigned to receive chewing gum (n=51) or routine postoperative care (n=58). Fewer participants assigned to receive chewing gum … experienced postoperative nausea (16 [31.4%] versus 29 [50.0%]; P=0.049) and postoperative ileus (0 vs. 5 [8.6%]; P=0.032).* There were no differences in the need for postoperative antiemetics, episodes of postoperative vomiting, readmissions, repeat surgeries, time to first hunger, time to toleration of clear liquids, time to regular diet, time to first flatus, or time to discharge. Conclusion? Postop gum chewing is safe & lowers the incidence of nausea and ileus! (Jernigan, Chen, & Sewell, 2014. Retrieve from PubMed abstract)

Do you see the elements of an RCT in above?

Let’s break it down.

Randomizedmeans that 109 subjects were randomly divided into 2 or more groups. In above case, 51 subjects ended up in a gum chewing group & 58 were assigned to a routine care, no gum group. Randomization increases the chance that the groups will be similar in characteristics such as age, gender, etc. This allows us to assume that different outcomes between groups are caused by gum-chewing, not by differences in group characteristics.

Controlled means that 1 of the groups is used as a control group. It is a comparison group, like the no-gum , standard care group above

Trial means that it was a study. The researchers were testing (trying) an intervention and measuring the outcomes to see if it worked. In this case the intervention was gum chewing and the measure outcomes were nausea and ileus.

Why should you care about RCTs? Because RCTs are strong evidence that an intervention works (or doesn’t) for your patients

Critical Thinking Exercise: Go to http://www.ncbi.nlm.nih.gov/pubmed In the blank box at the very top enter a few key words about the problem in which you are interested + RCT. For example: music pain + RCT. Then read 1 or more of the abstracts looking for random assignment (randomized), control group, and whether it was a study (trial). You’re on your way! -Dr.H

*Note: You may remember from other blogs that p<.05 means the difference between groups is probably cause by the intervention—in this case gum chewing.